Credit card companies and merchants incur billions of dollars in losses each year from credit card fraud. Fraud is particularly high in the world of ecommerce where no credit card is actually presented for verification and the actual identity of the user performing the transaction is very difficult, if not impossible, to verify.
Some current fraud mitigation techniques are described below. One technique is for credit companies to request a cardholder's zip code and card CVV information to verify the identity of the cardholder. Unfortunately, most times when the card or the card number gets compromised, this information gets compromised along with it, thus reducing the efficacy of these additional pieces of info.
Another technique is for card companies to build detailed expensive models on card holder behaviors around purchase patterns (things they buy, stores they frequent, etc.) and geo movements (places the cardholder typically travels to). These behaviors are compiled over time and constantly evolve. The purchase models are used in detecting fraud early and alerting merchants and cardholders. However, the models take months if not years to be built for each user and often result in false positives when a user breaks their pattern.
In recent years, the sudden surge in flash commerce and hyper targeted marketing has resulted in user purchase patterns becoming more erratic and unpredictable. A “good deal” will make most people click “Buy” on items they otherwise would not have thought of buying. This shift in behavior often results in false negatives if the model is too rigid and renders the model useless approving all transactions if it is made too flexible. The current system results in higher fraud levels on one end and higher customer service costs and more customer dissatisfaction on the other.
Accordingly, it would be desirable to provide an improved method and system for electronic purchase fraud alerting.